Text Generation
Transformers
Safetensors
qwen2
coder
code
agent
conversational
text-generation-inference
Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdminReal/NexusCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AdminReal/NexusCoder") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AdminReal/NexusCoder") model = AutoModelForCausalLM.from_pretrained("AdminReal/NexusCoder", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AdminReal/NexusCoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AdminReal/NexusCoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AdminReal/NexusCoder
- SGLang
How to use AdminReal/NexusCoder with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "AdminReal/NexusCoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "AdminReal/NexusCoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AdminReal/NexusCoder with Docker Model Runner:
docker model run hf.co/AdminReal/NexusCoder
File size: 13,572 Bytes
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Nexus Agent v0.2 - AI Agent với Skills + Tools
===============================================
Major upgrade từ v0.1:
- Tích hợp SkillRegistry (15+ skills)
- Tích hợp ToolRegistry (15+ tools)
- Memory system
- Planner cho multi-step tasks
- Tool routing thông minh
- Safety guardrails
- Audit logging
"""
from __future__ import annotations
from typing import Optional, List, Dict, Any, Callable
import json
import os
from datetime import datetime
from pathlib import Path
from ..config import NexusConfig
from ..inference.generator import NexusGenerator, DEFAULT_SYSTEM_PROMPT
from ..tokenizer.tokenizer import NexusTokenizer
from ..model.nexus_coder import NexusCoderForCausalLM
from .. import AUTHOR_INFO
from ..skills import SkillRegistry, get_global_registry as get_skill_registry
from ..tools import ToolRegistry, ToolContext, get_global_registry as get_tool_registry
from ..safety import SafetyFilter, get_default_guardrails
from .memory import ConversationMemory
from .planner import TaskPlanner
from .router import ToolRouter
class NexusAgent:
"""AI Agent v0.2 - Wrapper cấp cao cho Nexus Coder.
Features:
- Skill-based routing (15+ skills)
- Tool use (15+ tools)
- Conversation memory
- Task planning
- Safety guardrails
- Audit logging
Usage:
agent = NexusAgent()
agent.chat() # Interactive
# or
response = agent.respond("Viết hàm fibonacci")
"""
def __init__(
self,
generator: Optional[NexusGenerator] = None,
config: Optional[NexusConfig] = None,
name: str = "Nexus",
personality: str = "humorous",
language: str = "bilingual",
enable_logging: bool = True,
log_dir: str = "./logs",
enable_skills: bool = True,
enable_tools: bool = True,
enable_memory: bool = True,
enable_planner: bool = True,
enable_safety: bool = True,
working_dir: str = ".",
):
self.config = config or NexusConfig()
self.name = name
self.personality = personality
self.language = language
self.author_info = AUTHOR_INFO
self.working_dir = working_dir
# Generator (model + tokenizer)
if generator is None:
self.generator = NexusGenerator(
model=NexusCoderForCausalLM(self.config),
tokenizer=NexusTokenizer(),
config=self.config,
)
else:
self.generator = generator
# Logging
self.enable_logging = enable_logging
self.log_dir = log_dir
if enable_logging:
os.makedirs(log_dir, exist_ok=True)
# Skills (v0.2 NEW)
self.enable_skills = enable_skills and self.config.enable_skills
self.skill_registry: Optional[SkillRegistry] = (
get_skill_registry() if self.enable_skills else None
)
# Tools (v0.2 NEW)
self.enable_tools = enable_tools and self.config.enable_tools
self.tool_registry: Optional[ToolRegistry] = (
get_tool_registry() if self.enable_tools else None
)
self.tool_router = ToolRouter(self.tool_registry) if self.tool_registry else None
# Memory (v0.2 NEW)
self.enable_memory = enable_memory and self.config.enable_memory
self.memory = ConversationMemory() if self.enable_memory else None
# Planner (v0.2 NEW)
self.enable_planner = enable_planner and self.config.enable_planner
self.planner = TaskPlanner() if self.enable_planner else None
# Safety (v0.2 NEW)
self.enable_safety = enable_safety and self.config.enable_safety_filter
self.safety_filter = SafetyFilter() if self.enable_safety else None
self.guardrails = get_default_guardrails() if self.enable_safety else None
# Stats
self._stats = {
"total_messages": 0,
"skills_used": 0,
"tools_called": 0,
"safety_blocks": 0,
"session_start": datetime.now().isoformat(),
}
print(f"✓ Nexus Agent v0.2 initialized")
print(f" Tên: {self.name}")
print(f" Tác giả: {self.author_info['name']}")
print(f" Phiên bản: {self.author_info['version']}")
print(f" Skills: {len(self.skill_registry) if self.skill_registry else 0}")
print(f" Tools: {len(self.tool_registry) if self.tool_registry else 0}")
print(f" Memory: {'✓' if self.memory else '✗'}")
print(f" Planner: {'✓' if self.planner else '✗'}")
print(f" Safety: {'✓' if self.safety_filter else '✗'}")
def respond(self, user_input: str, **kwargs) -> str:
"""Phản hồi tin nhắn từ người dùng."""
start_time = datetime.now()
self._stats["total_messages"] += 1
# Safety check (input)
if self.guardrails:
guard_result = self.guardrails.check(user_input)
if not guard_result["allowed"]:
self._stats["safety_blocks"] += 1
return f"⚠️ {guard_result['message']}"
# Add to memory
if self.memory:
self.memory.add(role="user", content=user_input)
# Try skill routing
skill_used = None
skill_result = None
if self.skill_registry:
from ..skills.base import SkillContext
ctx = SkillContext(
prompt=user_input,
history=self.memory.get_history() if self.memory else [],
**kwargs,
)
skill = self.skill_registry.route(user_input, ctx)
if skill:
skill_used = skill.name
skill_result = skill.execute(ctx)
self._stats["skills_used"] += 1
# Check for tool calls in user input
tool_calls_made = []
if self.tool_router:
tool_calls = self.tool_router.detect_tool_calls(user_input)
for tc in tool_calls[:self.config.max_tool_calls]:
result = self.tool_registry.execute(
tc["name"],
tc.get("args", {}),
ToolContext(working_dir=self.working_dir),
)
tool_calls_made.append({
"tool": tc["name"],
"success": result.success,
"output": result.output[:500] if result.output else "",
})
self._stats["tools_called"] += 1
# Generate response
try:
# Build enhanced prompt with skill/tool context
enhanced_input = user_input
if skill_result:
enhanced_input += f"\n\n[Skill: {skill_used}] {skill_result.output}"
if tool_calls_made:
enhanced_input += "\n\n[Tool results:]"
for tc in tool_calls_made:
enhanced_input += f"\n- {tc['tool']}: {tc['output'][:200]}"
response = self.generator.chat(enhanced_input, **kwargs)
except Exception as e:
response = f"⚠️ Xin lỗi, có lỗi xảy ra: {e}"
# Add to memory
if self.memory:
self.memory.add(role="assistant", content=response)
elapsed = (datetime.now() - start_time).total_seconds()
# Logging
if self.enable_logging:
self._log_interaction(
user_input=user_input,
response=response,
elapsed=elapsed,
skill_used=skill_used,
tools_used=[t["tool"] for t in tool_calls_made],
)
return response
def chat(self) -> None:
"""Bắt đầu chế độ chat tương tác."""
print("\n" + "=" * 70)
print(f" 🤖 {self.name} Agent v0.2.0")
print(f" Tác giả: {self.author_info['name']}")
print(f" Phiên bản: {self.author_info['version']}")
print(f" Ngôn ngữ: {'Song ngữ' if self.language == 'bilingual' else self.language}")
print(f" Skills: {len(self.skill_registry) if self.skill_registry else 0}")
print(f" Tools: {len(self.tool_registry) if self.tool_registry else 0}")
print("=" * 70)
print("Commands:")
print(" exit/quit - Thoát")
print(" reset - Xóa lịch sử")
print(" info - Thông tin model")
print(" skills - Liệt kê skills")
print(" tools - Liệt kê tools")
print(" stats - Thống kê session")
print("-" * 70 + "\n")
while True:
try:
user_input = input("\n🧑 Bạn: ").strip()
except (EOFError, KeyboardInterrupt):
print("\n\n👋 Tạm biệt!")
break
if not user_input:
continue
cmd = user_input.lower()
if cmd in ["exit", "quit"]:
print(f"\n👋 Tạm biệt! Hẹn gặp lại bạn. - {self.name}")
break
elif cmd == "reset":
if self.memory:
self.memory.clear()
self.generator.reset_conversation()
print("\n🔄 Đã xóa lịch sử trò chuyện.")
continue
elif cmd == "info":
self._print_info()
continue
elif cmd == "skills":
self._print_skills()
continue
elif cmd == "tools":
self._print_tools()
continue
elif cmd == "stats":
self._print_stats()
continue
response = self.respond(user_input)
print(f"\n🤖 {self.name}: {response}")
def _print_info(self) -> None:
"""In thông tin về model."""
stats = self.config.estimated_total_params()
print("\n" + "=" * 60)
print(f" Model: {self.author_info['model_name']}")
print(f" Agent: {self.author_info['agent_name']}")
print(f" Version: {self.author_info['version']}")
print(f" Tác giả: {self.author_info['name']}")
print(f" GitHub: {self.author_info['github']}")
print("-" * 60)
print(f" Tổng tham số: {stats['total_params_billion']:.2f}B")
print(f" Tham số active: {stats['active_params_billion']:.2f}B")
print(f" Context window: {self.config.max_position_embeddings:,} tokens")
print(f" Experts: {self.config.num_experts} (active: {self.config.num_active_experts})")
print(f" Python: 3.12.13")
print("=" * 60)
def _print_skills(self) -> None:
"""Liệt kê skills."""
if not self.skill_registry:
print("\n❌ Skills chưa được enable")
return
print("\n" + "=" * 60)
print(" Available Skills")
print("=" * 60)
by_cat = self.skill_registry.list_by_category()
for cat, skills in sorted(by_cat.items()):
print(f"\n [{cat.upper()}]")
for s in skills:
skill = self.skill_registry.get(s)
print(f" • {s}: {skill.description}")
print("\n" + "=" * 60)
def _print_tools(self) -> None:
"""Liệt kê tools."""
if not self.tool_registry:
print("\n❌ Tools chưa được enable")
return
print("\n" + "=" * 60)
print(" Available Tools")
print("=" * 60)
by_cat = self.tool_registry.list_by_category()
for cat, tools in sorted(by_cat.items()):
print(f"\n [{cat.upper()}]")
for t in tools:
tool = self.tool_registry.get(t)
safety_icon = {
"safe": "✓", "moderate": "⚠", "dangerous": "⚡", "destructive": "💀"
}.get(tool.safety.value, "?")
print(f" {safety_icon} {t}: {tool.description}")
print("\n" + "=" * 60)
def _print_stats(self) -> None:
"""In thống kê session."""
print("\n" + "=" * 60)
print(" Session Stats")
print("=" * 60)
for k, v in self._stats.items():
print(f" {k}: {v}")
print("=" * 60)
def _log_interaction(
self,
user_input: str,
response: str,
elapsed: float,
skill_used: Optional[str] = None,
tools_used: Optional[List[str]] = None,
) -> None:
"""Log tương tác vào file."""
log_file = os.path.join(self.log_dir, f"chat_{datetime.now().strftime('%Y%m%d')}.jsonl")
entry = {
"timestamp": datetime.now().isoformat(),
"user": user_input,
"assistant": response,
"elapsed_seconds": elapsed,
"skill_used": skill_used,
"tools_used": tools_used or [],
}
try:
with open(log_file, "a", encoding="utf-8") as f:
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
except Exception:
pass
def get_author_info(self) -> Dict[str, str]:
"""Trả về thông tin tác giả."""
return self.author_info
def get_stats(self) -> Dict[str, Any]:
"""Trả về stats."""
return dict(self._stats)
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